Alzheimer's disease (AD) is one of the most prevalent neurodegenerative diseases in elderly and the incidence of this disease is increasing with older ages. One of the hallmarks of AD is the accumulation of beta-amyloid plaques (aβ) in human brains. Most of prior brain imaging researchers used the clinical symptom based diagnosis without the confirmation of imaging or fluid Aβ information. In this work, we study hippocampus morphometry on a cohort consisting of Aβ positive AD (N = 151) and matched Aβ negative cognitively unimpaired subjects (N = 271) with Aβ positivity determined via florbetapir PET. The brain images are obtained from publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI). We compute our surface multivariate morphometry statistics from segmented hippocampus structure in structural MR images. With these features, we find statistically significant difference by using Hotelling's T2 tests. Meanwhile, we apply a patch-based analysis of sparse coding system for binary group classification and achieve an accuracy rate of 90.48%. Our results demonstrate that our surface multivariate morphometry statistics (MMS) perform better than traditional hippocampal volume measures in classification and it may be applied as a potential biomarker for distinguishing dementia due to AD from age matched normal aging individuals.